Manufacturers are racing toward a future defined by AI-enabled autonomy, but many are struggling to rewire their operating models fast enough to realize it. A new Gartner survey finds confidence slipping, even as expectations for machine-led production accelerate and pressure builds to modernize governance, skills, and factory decision-making. Nearly half of respondents doubt their current manufacturing strategies will deliver expected business outcomes over the next three years.
Automation Outlook Accelerates
According to the Gartner survey of 128 manufacturing and supply chain leaders, two-thirds of organizations are not pursuing the aggressive redesign required to scale advanced automation, even as AI, robotics, and machine vision systems gain traction across production environments. Respondents expect machines to handle 32% of manufacturing tasks by 2028, up from 21% today, while human involvement is expected to fall from 52% to 40% in the same period.
Those expectations mirror broader industry momentum. Recent industry reports point to rising investment in autonomous mobile robots, AI-driven quality inspection systems, and predictive maintenance platforms, particularly in sectors such as automotive and electronics. Yet Gartner notes that most operating models still reflect traditional governance structures and cost-control mindsets, which may slow deployment of analytics-driven decision cycles and cross-functional digital integration.
“Leadership wants to embrace future capabilities to increase competitiveness, yet 66% of survey respondents say integrating supply chain and manufacturing is the most significant challenge,” said Simon Jacobson, VP analyst at Gartner, in an official statement. Bridging that disconnect, between automation ambition and execution discipline, remains a structural hurdle across many global networks.
Gartner found that organizations reporting directly to a chief supply chain officer (CSCO) are significantly more likely to show alignment, investment coordination, and digital execution momentum compared with those reporting to the COO, highlighting how reporting lines and governance shape modernization outcomes.
Empowering Plants and Resetting Governance
Gartner’s guidance centers on elevating factory-level decision-making and modernizing production systems to support faster, data-led execution. The firm recommends:
1. Resetting expectations for plant leadership. Instead of command-and-control oversight, plant managers will need authority to make decentralized decisions supported by AI-driven insights and targeted digital skills.
2. Standardizing production systems. Harmonizing digital platforms, linking site initiatives with enterprise programs, and defining common KPIs are necessary to scale automation tools consistently across sites.
3. Reallocating decision rights. Moving authority closer to the shop floor, with governance designed around autonomy and accountability, can accelerate responsiveness and allow AI-enabled tools to deliver full value.
This shift echoes broader industry practice. Automotive manufacturers are increasingly equipping production teams with AI-enabled scheduling and defect-detection systems, allowing frontline operators to make live quality and throughput interventions. Semiconductor producers and industrial firms have similarly been expanding digital capabilities at plant level to shorten response cycles and reduce manual engineering burden.
Still, democratizing decision-making requires cultural change and worker reskilling, areas where execution often lags investment. Recent trade research highlights a widening technical skills gap in industrial settings, particularly around data interpretation and human-machine collaboration, adding pressure to workforce development initiatives.
Why Governance Choices Will Shape Technology ROI
One emerging tension worth watching is how capital will flow as automation ambitions rise. Recent earnings calls across advanced manufacturing point to CFOs scrutinizing digital and robotics programs not just for efficiency gains, but for measurable throughput, uptime, and working-capital impact. Governance models that accelerate plant-level autonomy will need equally rigorous financial cadence, linking AI-enabled decisions directly to unit-cost, inventory turns, and resilience metrics.
